Approximately 1.4 million women per year depend on pathologists to accurately interpret breast biopsies for a diagnosis of benign disease or cancer. Diagnostic errors are alarmingly frequent and likely lead to altered patient treatment, especially at the thresholds of atypical hyperplasia and ductal carcinoma in situ, where up to 50% of cases are misclassified. The causes underlying these errors remain largely unknown. Technology similar to Google Maps now allows pan and zoom manipulation of high-resolution digital images of glass microscope slides. This technology has virtually replaced the microscope in medical schools and is rapidly diffusing into U.S. pathology practices. No research has evaluated the accuracy and efficiency of pathologists' interpretion of digital images vs. glass slides. However, these digital slides offer a novel opportunity to study the accuracy, efficiency, and viewing behavior of a large number of pathologists as they manipulate and interpret images. The innovative analytic techniques proposed in this application are similar to those used to improve the performance of pilots and air traffic controllers. Our specific aims are: Aim 1. Digital Image vs. Glass Slides: To compare the interpretive accuracy of pathologists viewing digitized slide images over the Internet to their performance viewing original glass slides under a microscope. A randomized national sample of pathologists (N=200) will interpret 240 test cases in one or both formats in two phases. Measures will include a diagnostic assessment for each test case and for digital slides, cursor- (i.e., mouse) tracking data and region of interest (ROI) markings. Completion of this aim will establish benchmarks for the comparative diagnostic accuracy of whole-slide digital images. Aim 2. Interpretive Screening Behavior: To identify visual scanning patterns associated with diagnostic accuracy and efficiency. Detailed simultaneous eye-tracking and cursor-tracking data will be collected on 60 additional pathologists while they interpret digital slides to complement data from Aim 1. Viewing patterns will be analyzed from computer representations of raw movement data. Videos depicting accurate, efficient visual scanning and cursor movement will be valuable tools in educating the next generation of digital pathologists. Aim 3. Image Analyses: To examine and classify the image characteristics (including color, texture, and structure) of ROIs captured in Aims 1 and 2. Computer-based statistical learning techniques will be used to identify image characteristics that lead to correct and incorrect diagnoses. Characteristics of both diagnostic and distracting ROIs will be identified, linking all three aims. In summary, we will determine whether digitized whole-slide images are diagnostically equivalent to original glass slides. Our in-depth scientific evaluation of viewing patterns and characteristics of ROIs identified by pathologists will be critical to understanding diagnostic errors and sources of distraction. Optimization of viewing techniques will improve diagnostic performance and thus, the quality of clinical care.